Theoretical and Computational Advances of Molecular Reaction Dynamics provides a strong foundation and comprehensive review of the principles, formulations, and methodology of MRD, with detailed tutorial guides and case studies for practical application, whilst demonstrating recent developments. It is designed to help improve understanding of the full-dimension accurate potential of the energy surface, chemical kinetics, reaction dynamics, collision energy transfer, and molecular spectra using MRD techniques. Details are given for calculating various molecular dynamic properties for various prototypical reactions/chemical species efficiently and accurately. Useful and timely tutorials for the practical implementations with tips and usable codes are included for research in this multidisciplinary field. The book also familiarizes readers with state-of-the-art research frontiers on theoretical and computational MRD, showing the new methods, theories, applications, and advances that have developed for the investigation of chemical kinetics, reaction dynamics, and molecular spectra—at the microscopic atomic or molecular level—in the era of machine learning and big data.
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1. Introduction Section 1: Methodology 2. Potential energy surface 3. Quasi-Classical Trajectory 4. Quantum Dynamics 5. Ring Polymer Molecular Dynamics 6. Machine learning in molecular reaction dynamics Section 2: Case studies for bimolecular nonreactive dynamics 7. Bimolecular nonreactive dynamics Section 3: Case studies for prototypical reactions 8. Featured Bimolecular reactive dynamics 9. Photodetachment dynamics Section 4: Case studies for Kinetics (RPMD, QCT, wave package, and others) 10. RPMD kinetics Section 5: Conclusion, summary, and perspectives 11. Conclusion, summary, and perspectives
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Provides a comprehensive and practical overview of recent advances in computational molecular reaction dynamics
Summarizes recent advances in computational molecular reaction dynamics, offering learning about the connection between molecular reaction dynamics and novel data-driven modeling Applies recent advances in classical and quantum dynamics methodologies to deal with polyatomic molecular reactions Offers practical sample programs to help readers understand and use molecular reaction dynamic modelling effectively and efficiently
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Produktdetaljer

ISBN
9780443137990
Publisert
2025-08-01
Utgiver
Elsevier - Health Sciences Division; Elsevier - Health Sciences Division
Høyde
229 mm
Bredde
152 mm
Aldersnivå
P, 06
Språk
Product language
Engelsk
Format
Product format
Heftet
Antall sider
352

Om bidragsyterne

Jun Li is Professor of Chemistry at the School of Chemistry and Chemical Engineering, Chongqing University, PR China. He received his Bachelor and Doctoral degrees from Sichuan University. He worked with Professor Sheng-Hsien Lin as a visiting student at National Chiao-Tung University, PR China on photochemistry for five months. After a three-year postdoctoral research role with Prof. Hua Guo at University of New Mexico, USA, he joined Chongqing University as an independent PI. With a Humboldt Fellowship for Experienced Researchers, he then worked with Professor Jörg Behler at Georg-August-Universität Göttingen, Germany. He has published more than 120 peer-reviewed articles and 2 book chapters “Data Quality, Data Sampling and Data Fitting: A Tutorial Guide for Constructing Full-dimensional Accurate Potential Energy Surfaces (PESs) of Small Molecular Systems” by Jun Li*, Yang Liu, in the book entitled Machine Learning in Molecular Sciences (Springer Nature, 2023) and “Tunneling in unimolecular and bimolecular reactions” by Hua Guo, Jianyi Ma, and Jun Li in Molecular Quantum Dynamics: From Theory to Applications (Physical Chemistry in Action), Edited by Fabien Gatti, Springer 2014. His research interests include potential energy surfaces, reaction kinetics, dynamics, and atomic-level mechanisms, mainly for gas phase systems, and machine learning. Hongwei Song received his PhD at Nanyang Technological University, PR China in 2012 under the supervision of Prof. Soo-Ying Lee. He then worked as a Postdoctoral Research Fellow with Professor Hua Guo at University of New Mexico, USA. In 2015, he joined the faculty of Innovation Academy for Precision Measurement Science and Technology of the Chinese Academy of Sciences (Wuhan Institute of Physics and Mathematics before 2019), PR China. His research interests involve ab initio potential energy surface, classical and quantum molecular reaction dynamics, and machine learning. Yongle Li received his B.S. degree from Tianjin University, PR China in 2006, and a Ph.D. degree from Nanjing University, PR China in 2011, under supervision of John Z. H. Zhang and Daiqian Xie. After working in East China Normal University, PR China for a short period, he worked as a Research Associate at the University of New Mexico, USA in 2012–2013 in Hua Guo’s group and New York University, USA in 2013–2015 in Yingkai Zhang’s group. In 2019-2021, he was a visiting associate at CalTech, USA in Tom Miller III’s group. Now he is an Associate Professor in Shanghai University, PR China as a Young Eastern Scholar. His group focuses on quantum/classical dynamics simulations, including reaction rates using RPMD and phase transition of molecular ferroelectrics.